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Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks

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Abstract

Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The economic burden of COPD grows continuously because it is not a curable disease. These conditions make COPD an important research field of artificial intelligence (AI) techniques in medicine. In this study, an integrated approach of the statistical-based fuzzy cognitive maps (SBFCM) and artificial neural networks (ANN) is proposed for predicting length of hospital stay of patients with COPD, who admitted to the hospital with an acute exacerbation. The SBFCM method is developed to determine the input variables of the ANN model. The SBFCM conducts statistical analysis to prepare preliminary information for the experts and then collects expert opinions accordingly, to define a conceptual map of the system. The integration of SBFCM and ANN methods provides both statistical data and expert opinion in the prediction model. In the numerical application, the proposed approach outperformed the conventional approach and other machine learning algorithms with 79.95% accuracy, revealing the power of expert opinion involvement in medical decisions. A medical decision support framework is constructed for better prediction of length of hospital stay and more effective hospital management.

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Acknowledgments

The authors are grateful to chest disease specialists Assoc. Prof. Gülfidan Aras MD and Zehra Dilek Kanmaz MD, who contributed with their valuable insight and expertise. The authors also thank to Özlem Yılmaz Ünlü MD for her contribution in collecting patient data.

Funding

This study is supported by the Galatasaray University Research Fund.

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Authors and Affiliations

  1. Industrial Engineering Dept., Galatasaray University, Ciragan Cad. No.: 36, Ortakoy, 34349, Istanbul, Turkey

    Elif Dogu & Y. Esra Albayrak

  2. Yedikule Chest Diseases & Thoracic Surgery Training & Research Hospital, Belgrad Kapi Yolu Cad. No.: 1 34020 Zeytinburnu, Istanbul, Turkey

    Esin Tuncay

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  1. Elif Dogu
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Correspondence to Elif Dogu.

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Dogu, E., Albayrak, Y.E. & Tuncay, E. Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks. Med Biol Eng Comput 59, 483–496 (2021). https://doi.org/10.1007/s11517-021-02327-9

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  • DOI: https://doi.org/10.1007/s11517-021-02327-9

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